{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "sizes = np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])\n",
    "\n",
    "plt.scatter(x, y, s=(y-min(y))*10)\n",
    "\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
